Chemical-oriented simulation of computational systems with ALCHEMIST

Pianini, Danilo; Montagna, Sara; Viroli, Mirko

doi:10.1057/jos.2012.27

Cited by 113 publications

(75 citation statements)

References 37 publications

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“…where N is the number of devices, T the number of targets, and c d the actual target count at device d. We run our experiments on the Alchemist simulator [16].…”

Section: A Experimental Setupmentioning

confidence: 99%

Self-Stabilising Target Counting in Wireless Sensor Networks Using Euler Integration

Pianini

Dobson

Viroli

2017

2017 IEEE 11th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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Abstract-Target counting is an established challenge for sensor networks: given a set of sensors that can count (but not identify) targets, how many targets are there? The problem is complicated because of the need to disambiguate duplicate observations of the same target by different sensors. A number of approaches have been proposed in the literature, and in this paper we take an existing technique based on Euler integration and develop a fully-distributed, self-stabilising solution. We derive our algorithm within the field calculus from the centralised presentation of the underlying integration technique, and analyse the precision of the counting through simulation of several network configurations.

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“…where N is the number of devices, T the number of targets, and c d the actual target count at device d. We run our experiments on the Alchemist simulator [16].…”

Section: A Experimental Setupmentioning

confidence: 99%

Self-Stabilising Target Counting in Wireless Sensor Networks Using Euler Integration

Pianini

Dobson

Viroli

2017

2017 IEEE 11th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)

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“…MultiVeStA currently supports the simulators Alchemist [24], DEUS [4], MISSCEL [11], and the two originally supported ones [32] (i.e. PMaude and a CTMC engine).…”

Section: Discussionmentioning

confidence: 99%

“…Essentially, the requirements are: (1) the support for the step-by-step simulations, and (2) the ability to inspect the states of the simulations to obtain real-typed observations. The downloadable version of MultiVeStA [23] supports some simulators, namely Alchemist [24], DEUS [4], MISSCEL [11], and the two originally supported ones [32] (i.e. PMaude and a CTMC engine).…”

Section: Integrating a Simulator And Its Modelsmentioning

confidence: 99%

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MultiVeStA: Statistical Model Checking for Discrete Event Simulators

Sebastio

Vandin

2014

Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools

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The modeling, analysis and performance evaluation of largescale systems are difficult tasks. Due to the size and complexity of the considered systems, an approach typically followed by engineers consists in performing simulations of systems models to obtain statistical estimations of quantitative properties. Similarly, a technique used by computer scientists working on quantitative analysis is Statistical Model Checking (SMC), where rigorous mathematical languages (typically logics) are used to express systems properties of interest. Such properties can then be automatically estimated by tools performing simulations of the model at hand. These property specifications languages, often not popular among engineers, provide a formal, compact and elegant way to express systems properties without needing to hard-code them in the model definition. This paper presents MultiVeStA, a statistical analysis tool which can be easily integrated with existing discrete event simulators, enriching them with efficient distributed statistical analysis and SMC capabilities.

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“…In this simulation, people move at an average 1 m/s, the docent and all patrons carry personal devices running the virtual machine, executing asynchronously at 10Hz, and communicating via low-power Bluetooth to a range of 10 meters. The simulation was implemented using the ALCHEMIST [18] simulation framework and the Protelis [17] incarnation of field calculus, updated to the extended version of the calculus presented in this paper.…”

Section: Simulated Example Applicationmentioning

confidence: 99%

Code Mobility Meets Self-organisation: A Higher-Order Calculus of Computational Fields

Damiani¹,

Viroli

Pianini

et al. 2015

Formal Techniques for Distributed Objects, Components, and Systems

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Abstract. Self-organisation mechanisms, in which simple local interactions result in robust collective behaviors, are a useful approach to managing the coordination of large-scale adaptive systems. Emerging pervasive application scenarios, however, pose an openness challenge for this approach, as they often require flexible and dynamic deployment of new code to the pertinent devices in the network, and safe and predictable integration of that new code into the existing system of distributed self-organisation mechanisms. We approach this problem of combining self-organisation and code mobility by extending "computational field calculus", a universal calculus for specification of self-organising systems, with a semantics for distributed first-class functions. Practically, this allows selforganisation code to be naturally handled like any other data, e.g., dynamically constructed, compared, spread across devices, and executed in safely encapsulated distributed scopes. Programmers may thus be provided with the novel firstclass abstraction of a "distributed function field", a dynamically evolving map from a network of devices to a set of executing distributed processes.

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Chemical-oriented simulation of computational systems with ALCHEMIST

Cited by 113 publications

References 37 publications

Self-Stabilising Target Counting in Wireless Sensor Networks Using Euler Integration

Self-Stabilising Target Counting in Wireless Sensor Networks Using Euler Integration

MultiVeStA: Statistical Model Checking for Discrete Event Simulators

Code Mobility Meets Self-organisation: A Higher-Order Calculus of Computational Fields

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